Abstract: A device may receive sensor data and video data associated with a vehicle, and may process the sensor data, with a rule-based detector model, to determine whether a probability of a vehicle accident satisfies a first threshold. The device may preprocess acceleration data of the sensor data to generate calibrated acceleration data, and may process the calibrated acceleration data, with an anomaly detector model, to determine whether the calibrated acceleration data includes anomalies. The device may filter the sensor data to generate filtered sensor data, and may process the filtered sensor data and anomaly data, with a decision model, to determine whether the probability of the vehicle accident satisfies a second threshold. The device may process the filtered sensor data, the anomaly data, and the video data, with a machine learning model, to determine whether the vehicle accident has occurred, and may perform one or more actions.

Abstract: A server includes: a memory storing printing configuration rules for interfacing with a printing system; a communications interface configured to communicate with a client device; a processor interconnected with the memory and the communications interface, the processor configured to: generate a print system network identifier mapped to the printing system, the print system network identifier configured for identifying print parameters for print jobs; receive, via the communications interface, a print request from the client device, the print request generated based on the print system network identifier and including one or more print parameters for a print job; and send a print instruction to the printing system to initiate the print job at the printing system according to the one or more print parameters, the print instruction generated according to the printing configuration rules.

Abstract: Systems, methods, and other embodiments described herein relate to generating depth estimates of an environment depicted in a monocular image. In one embodiment, a method includes identifying semantic features in the monocular image according to a semantic model. The method includes injecting the semantic features into a depth model using pixel-adaptive convolutions. The method includes generating a depth map from the monocular image using the depth model that is guided by the semantic features. The pixel-adaptive convolutions are integrated into a decoder of the depth model. The method includes providing the depth map as the depth estimates for the monocular image.

Abstract: The present invention relates to the field of medical monitoring, and in particular non-contact, video-based monitoring of pulse rate, respiration rate, motion, and oxygen saturation. Systems and methods are described for capturing images of a patient, producing intensity signals from the images, filtering those signals to focus on a physiologic component, and measuring a vital sign from the filtered signals.

Abstract: A learning model generating device includes a first image reading device and a first control device. The first control device includes a processor and functions, through the processor executing a first control program, as a first segmenter, a learning model generator, and a first compressor. The first segmenter segments each of images of training prints obtained by reading performed by the first image reading device. The learning model generator learns segmented images to generate a first learning model for use in inferring a type of an image defect. The first compressor compresses each of the images of the training prints. The first segmenter segments each of compressed images obtained by compression performed by the first compressor. The learning model generator learns compressed and segmented images to generate a second learning model for use in inferring a type of an image defect.

Abstract: An information processing apparatus is connected to a first information acquisition apparatus that acquires first information relating to at least one of a position or a pose of a hand of a target person and a second information acquisition apparatus that acquires second information relating to at least one of the position or the pose of the hand of the target person and different from the first information acquired by the first information acquisition apparatus. The information processing apparatus accepts the first information and the second information from the first and second information acquisition apparatuses, respectively, retains the accepted first and second information in an associated relationship with information of timings at which the first and second information acquisition apparatuses acquire the first and second information, respectively, and extracts pieces of the first and second information acquired at a common timing from the retained first and second information as pair information.

Abstract: This document describes techniques and systems for authentication management through IMU and radar. The techniques and systems use inertial sensor data from an inertial measurement unit (IMU) and/or radar data to manage authentication for a computing device. By so doing, the techniques conserve power, improve accuracy, or reduce latency relative to many common techniques and systems for computing-device authentication.

Abstract: A method for urban scene reconstruction uses the top view of a scene as priori information to generate a UVA initial flight path, optimizes the initial path in real time, and realizes 3D reconstruction of the urban scene. There are four steps: (1): to analyze the top view of a scene, obtain the scene layout, and generate a UAV initial path; (2): to reconstruct the sparse point cloud of the building and estimate the building height according to the initial path, combine the scene layout to generate a rough scene model, and adjust the initial path height; (3): to use the rough scene model, sparse point cloud and the UAV flight trajectory to obtain the scene coverage confidence map and the details that need close-ups, optimize the flight path in real time; and (4): to obtain high resolution images, reconstruct them to obtain a 3D model of the scene.

Abstract: A machine learning system includes a first determination model that determines whether an input image is a second domain image, and a second determination model that determines whether an extracted image obtained by extracting an area where an object is presented from the input image is from the second domain image. Either a pseudo second domain image or the second domain image is selected and input into the first determination model, and either a first extracted image in the pseudo second domain image or a second extracted image in the second domain image is selected and input into an image extracting unit. Learning of the first determination model is performed based on a first determination result, learning of the second determination model is performed based on a second determination result, and learning of a pseudo image generative model is performed based on the first and second determination results.

Abstract: A non-boarded passenger search device which searches for a non-boarded passenger includes an image registration unit that registers image data of faces of a plurality of scheduled passengers photographed before a predetermined passage point; a determination unit that determines whether a scheduled passenger whose face is photographed at the passage point is registered in the image registration unit; when the face is photographed at the passage point is registered, a fight number registration unit that registers a flight number which the scheduled passenger is scheduled to board, in association with image data of the face; and a presentation unit that presents position information of the scheduled passenger, based on the image data of the face of the scheduled passenger, the flight number, image data of the face of the scheduled passenger and position information of the plurality of photographing points.

Abstract: Methods and apparatus for compressing image data are described along with corresponding methods and apparatus for decompressing the compressed image data. An encoder unit, which generates the compressed image data, comprises an input arranged to receive a first image and a second image, wherein the second image is twice the width and height of the first image, a prediction generator arranged to generate a prediction texture from the first image using an adaptive interpolator, a difference texture generator arranged to generate a difference texture from the prediction texture and the second image and in encoder unit arranged to encode the difference texture.

Abstract: A drone which carries out surveillance by surveilling the user's property. Items in the property can be mapped by the drone. People come on the property can be imaged, and use image processing to compare the image of the people with known images of known people. An alarm can because when an unknown person comes. When the packages delivered, the drone can carry out surveillance on the package, including imaging the outside of the package in the inside of the package.

Abstract: The system and method are directed to a prototypical contrastive learning (PCL). The PCL explicitly encodes the hierarchical semantic structure of the dataset into the learned embedding space and prevents the network from exploiting low-level cues for solving the unsupervised learning task. The PCL includes prototypes as the latent variables to help find the maximum-likelihood estimation of the network parameters in an expectation-maximization framework. The PCL iteratively performs an E-step for finding prototypes with clustering and M-step for optimizing the network on a contrastive loss.

Abstract: Provided is a system protecting privacy or the like of a land owner when providing an image taken by an unmanned flying object to the public (1) includes an unmanned aerial vehicle (10) and a computer (30). A control unit (14) in the unmanned aerial vehicle (10) executes an image acquisition module (141) to acquire an aerial image including positional information. A control unit (32) in the computer (30) executes a region designation module (321) to set a region where viewing is to be restricted. The control unit (32) executes a condition setting module (332) to set conditions for canceling the viewing restriction and set the conditions in a viewing authorization database (332). The control unit (32) executes a provision module (323) to provide an aerial image which includes the region where viewing is restricted.

Abstract: An image forming apparatus includes: a print engine to perform printing; a memory that stores a plurality of print conditions set for each of a plurality of direct printing methods; and circuitry to: receive print information on printing to be performed, the print information at least indicating image data to be printed; identify a direct printing method from the print information; acquire from the memory at least one print condition corresponding to the identified direct printing method; and control the print engine to perform printing based on image data according to the acquired print condition.

Abstract: According to the present disclosure, an apparatus for real-time object detection in an image captured by an unmanned aerial vehicle (UAV) including an image splitter configured to receive an input image and split the input image into a left image and a right image; and an object detector configured to detect an object from each of the left image and the right image provided from the image splitter is provided.

Abstract: A tracking method includes obtaining first characteristic information of a target object through a first camera. The tracking method also includes obtaining second characteristic information of the target object through a second camera. The tracking method further includes tracking the target object based on at least one of the first characteristic information or the second characteristic information.

Abstract: An information processing apparatus includes: a processor configured to: partition an image of a structure into regions; detect, for target regions each including crack points among the regions, a representative line segment representing the crack points included in each target region as a basic line segment for each target region; generate, based on basic line segments whose end points are close to each other among the detected basic line segments, a tree structure having an end point of a specific basic line segment as a root; and detect, among routes in the generated tree structure, routes from a top to a bottom of the tree structure corresponding to a basic line segment group in which values indicating degrees of coupling probability between the basic line segments are highest and a total length of the basic line segments coupled is longest, as a stroke corresponding to crack lines.

Abstract: Methods and apparatus related to generating a model for an object encountered by a robot in its environment, where the object is one that the robot is unable to recognize utilizing existing models associated with the robot. The model is generated based on vision sensor data that captures the object from multiple vantages and that is captured by a vision sensor associated with the robot, such as a vision sensor coupled to the robot. The model may be provided for use by the robot in detecting the object and/or for use in estimating the pose of the object.

Abstract: An information processing device extracts an image of a marker from a photographed image, and obtains a position of a representative point of the marker in a three-dimensional space. Meanwhile, a position and an attitude corresponding to a time of photographing the image are estimated on the basis of an output value of a sensor included in a target object. A weight given to positional information of each marker is determined by using a target object model on the basis of the estimation, and positional information of the target object is calculated. Further, final positional information is obtained by synthesizing estimated positional information at a predetermined ratio, and the final positional information is output and fed back for a next estimation.